Abstract
Micro-finance organizations provide non-profit lending opportunities to mitigate poverty by financially supporting impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to them. In Kiva.org, a widely-used crowd-funded micro-financial service, a vast amount of micro-financial activities are done by lending teams, and thus, understanding their diverse characteristics is crucial in maintaining a healthy micro-finance ecosystem. As the first step for this goal, we model different lending teams by using a maximum-entropy distribution approach based on a wealthy set of heterogeneous information regarding microfinancial transactions available at Kiva. Based on this approach, we achieved a competitive performance in predicting the lending activities for the top 200 teams. Furthermore, we provide deep insight about the characteristics of lending teams by analyzing the resulting team-specific lending models. We found that lending teams are generally more careful in selecting loans by a loan's geo-location, a borrower's gender, a field partner's reliability, etc., when compared to lenders without team affiliations. In addition, we identified interesting lending behaviors of different lending teams based on lenders' background and interest such as their ethnic, religious, linguistic, educational, regional, and occupational aspects. Finally, using our proposed model, we tackled a novel problem of lending team recommendation and showed its promising performance results. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
| Original language | English |
|---|---|
| Title of host publication | WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 249-259 |
| Number of pages | 11 |
| ISBN (Electronic) | 9781450327442 |
| DOIs | |
| Publication status | Published - 2014 Apr 7 |
| Externally published | Yes |
| Event | 23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of Duration: 2014 Apr 7 → 2014 Apr 11 |
Publication series
| Name | WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web |
|---|
Other
| Other | 23rd International Conference on World Wide Web, WWW 2014 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 14/4/7 → 14/4/11 |
Fingerprint
Keywords
- Community characteristics
- Heterogeneous feature
- Maximum entropy distribution
- Microfinance
ASJC Scopus subject areas
- Computer Networks and Communications
- Software
Cite this
To gather together for a better world : Understanding and leveraging communities in micro-lending recommendation. / Choo, Jaegul; Lee, Daniel; Dilkina, Bistra; Zha, Hongyuan; Park, Haesun.
WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web. Association for Computing Machinery, Inc, 2014. p. 249-259 (WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - To gather together for a better world
T2 - Understanding and leveraging communities in micro-lending recommendation
AU - Choo, Jaegul
AU - Lee, Daniel
AU - Dilkina, Bistra
AU - Zha, Hongyuan
AU - Park, Haesun
PY - 2014/4/7
Y1 - 2014/4/7
N2 - Micro-finance organizations provide non-profit lending opportunities to mitigate poverty by financially supporting impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to them. In Kiva.org, a widely-used crowd-funded micro-financial service, a vast amount of micro-financial activities are done by lending teams, and thus, understanding their diverse characteristics is crucial in maintaining a healthy micro-finance ecosystem. As the first step for this goal, we model different lending teams by using a maximum-entropy distribution approach based on a wealthy set of heterogeneous information regarding microfinancial transactions available at Kiva. Based on this approach, we achieved a competitive performance in predicting the lending activities for the top 200 teams. Furthermore, we provide deep insight about the characteristics of lending teams by analyzing the resulting team-specific lending models. We found that lending teams are generally more careful in selecting loans by a loan's geo-location, a borrower's gender, a field partner's reliability, etc., when compared to lenders without team affiliations. In addition, we identified interesting lending behaviors of different lending teams based on lenders' background and interest such as their ethnic, religious, linguistic, educational, regional, and occupational aspects. Finally, using our proposed model, we tackled a novel problem of lending team recommendation and showed its promising performance results. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
AB - Micro-finance organizations provide non-profit lending opportunities to mitigate poverty by financially supporting impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to them. In Kiva.org, a widely-used crowd-funded micro-financial service, a vast amount of micro-financial activities are done by lending teams, and thus, understanding their diverse characteristics is crucial in maintaining a healthy micro-finance ecosystem. As the first step for this goal, we model different lending teams by using a maximum-entropy distribution approach based on a wealthy set of heterogeneous information regarding microfinancial transactions available at Kiva. Based on this approach, we achieved a competitive performance in predicting the lending activities for the top 200 teams. Furthermore, we provide deep insight about the characteristics of lending teams by analyzing the resulting team-specific lending models. We found that lending teams are generally more careful in selecting loans by a loan's geo-location, a borrower's gender, a field partner's reliability, etc., when compared to lenders without team affiliations. In addition, we identified interesting lending behaviors of different lending teams based on lenders' background and interest such as their ethnic, religious, linguistic, educational, regional, and occupational aspects. Finally, using our proposed model, we tackled a novel problem of lending team recommendation and showed its promising performance results. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
KW - Community characteristics
KW - Heterogeneous feature
KW - Maximum entropy distribution
KW - Microfinance
UR - http://www.scopus.com/inward/record.url?scp=84909592436&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84909592436&partnerID=8YFLogxK
U2 - 10.1145/2566486.2568020
DO - 10.1145/2566486.2568020
M3 - Conference contribution
AN - SCOPUS:84909592436
T3 - WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web
SP - 249
EP - 259
BT - WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web
PB - Association for Computing Machinery, Inc
ER -